Enhancing Task-Oriented Dialogue Systems through Linguistic Entrainment

Linguistic entrainment, where conversational participants align their linguistic patterns, can improve the naturalness and success of task-oriented dialogue systems. This work introduces methods to achieve dialogue entrainment in a GPT-2-based end-to-end system through training instance weighting, an entrainment-specific loss, and keyword-based generation conditioning.